def test_dcae(): """ Tests that DeepComposedAutoencoder calls the Model superclass constructor """ ae = Autoencoder(5, 7, act_enc='tanh', act_dec='cos', tied_weights=True) model = DeepComposedAutoencoder([ae]) model._ensure_extensions()
def test_dcae(): """ Tests that DeepComposedAutoencoder works correctly """ ae = Autoencoder(5, 7, act_enc="tanh", act_dec="cos", tied_weights=True) model = DeepComposedAutoencoder([ae]) model._ensure_extensions() data = np.random.randn(10, 5).astype(config.floatX) model.perform(data)
def train_model(): global ninput, noutput simdata = SimulationData( sim_path="../../javaDataCenter/generarDadesV1/CA_SDN_topo1/") simdata.load_data() simdata.preprocessor() dataset = simdata.get_matrix() structure = get_structure() layers = [] for pair in structure: layers.append(get_autoencoder(pair)) model = DeepComposedAutoencoder(layers) training_alg = SGD(learning_rate=1e-3, cost=MeanSquaredReconstructionError(), batch_size=1296, monitoring_dataset=dataset, termination_criterion=EpochCounter(max_epochs=50)) extensions = [MonitorBasedLRAdjuster()] experiment = Train(dataset=dataset, model=model, algorithm=training_alg, save_path='training2.pkl', save_freq=10, allow_overwrite=True, extensions=extensions) experiment.main_loop()
def test_dcae(): """ Tests that DeepComposedAutoencoder works correctly """ ae = Autoencoder(5, 7, act_enc='tanh', act_dec='cos', tied_weights=True) model = DeepComposedAutoencoder([ae]) model._ensure_extensions() data = np.random.randn(10, 5).astype(config.floatX) model.perform(data)
def get_model(self): self.model = DeepComposedAutoencoder(self.layers) return self.model